Optimization of the Data Treatment Steps of a Non-targeted LC-MS-Based Workflow for the Identification of Trace Chemical Residues in Honey

  • Annie von Eyken
  • Stéphane BayenEmail author
Research Article


Non-targeted screening (e.g., suspected-target) is emerging as an attractive tool to investigate the occurrence of contaminants in food. The sample preparation and instrument analysis steps are known to influence the identification of analytes with non-targeted workflows, especially for complex matrices. However, for methods based on mass spectrometry, the impact of the post-analysis data treatment (e.g., feature extraction) on the capacity to correctly identify a contaminant at trace level is currently not well understood. The aim of the study was to investigate the influence of seven post-analysis data treatment parameters on the non-targeted identification of trace contaminants in honey using high-performance liquid chromatography coupled to hybrid quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS). Seven compounds reported as veterinary drugs for honeybees were applied as model compounds. Among the parameters studied, the expansion window for chromatogram extraction and the average scans included in the spectra influenced significantly the identification process results. The optimized data treatment was applied to the non-targeted screening of veterinary drugs, pesticides, and other contaminants in 55 honey samples as a proof of concept. Among the 43 compounds included in a library of honey-related compounds that was used for screening, eight compounds were tentatively identified in at least one honey sample. The tentative identity of two of these compounds (tylosin A and hydroxymethylfurfural) was further confirmed with analytical standards.

Graphical Abstract


Suspected screening Food contaminants HPLC-QTOF-MS Data-independent Acquisition 



We wish to acknowledge the technical support received from Jerry Zweigenbaum and Jean-François Roy from Agilent Technologies and the financial support from the Fonds de recherche du Québec – Nature et technologies (FRQNT) research grant (FRQ-NT NC-198270) and the Canada Foundation for Innovation/John R. Evans Leaders Fund grant (Project no. 35318) to S. Bayen.

Supplementary material

13361_2019_2157_MOESM1_ESM.docx (85 kb)
ESM 1 (DOCX 85 kb)


  1. 1.
    Krauss, M., Singer, H., Hollender, J.: LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns. Anal. Bioanal. Chem. 397, 943–951 (2010)CrossRefGoogle Scholar
  2. 2.
    Mollerup, C.B., Dalsgaard, P.W., Mardal, M., Linnet, K.: Targeted and non-targeted drug screening in whole blood by UHPLC-TOF-MS with data-independent acquisition. Drug Test. Anal. 9, 1052–1061 (2017)CrossRefGoogle Scholar
  3. 3.
    Garcia-Reyes, J.F., Hernando, M.D., Molina-Diaz, A., Fernandez-Alba, A.R.: Comprehensive screening of target, non-target and unknown pesticides in food by LC-TOF-MS. Trac-Trend. Anal. Chem. 26, 828–841 (2007)CrossRefGoogle Scholar
  4. 4.
    Diaz, R., Ibanez, M., Sancho, J.V., Hernandez, F.: Target and non-target screening strategies for organic contaminants, residues and illicit substances in food, environmental and human biological samples by UHPLC-QTOF-MS. Anal. Methods. 4, 196–209 (2012)CrossRefGoogle Scholar
  5. 5.
    Baduel, C., Mueller, J.F., Tsai, H.H., Ramos, M.J.G.: Development of sample extraction and clean-up strategies for target and non-target analysis of environmental contaminants in biological matrices. J. Chromatogr. A. 1426, 33–47 (2015)CrossRefGoogle Scholar
  6. 6.
    Khakimov, B., Gurdeniz, G., Engelsen, S.B.: Trends in the application of chemometrics to foodomics studies. Acta Aliment. 44, 4–31 (2015)CrossRefGoogle Scholar
  7. 7.
    Malato, O., Lozano, A., Mezcua, M., Aguera, A., Fernandez-Alba, A.R.: Benefits and pitfalls of the application of screening methods for the analysis of pesticide residues in fruits and vegetables. J. Chromatogr. A. 1218, 7615–7626 (2011)CrossRefGoogle Scholar
  8. 8.
    Knolhoff, A.M., Zweigenbaum, J.A., Croley, T.R.: Nontargeted screening of food matrices: development of a chemometric software strategy to identify unknowns in liquid chromatography-mass spectrometry data. Anal. Chem. 88, 3617–3623 (2016)CrossRefGoogle Scholar
  9. 9.
    Knolhoff, A.M., Callahan, J.H., Croley, T.R.: Mass accuracy and isotopic abundance measurements for HR-MS instrumentation: capabilities for non-targeted analyses. J. Am. Soc. Mass Spectr. 25, 1285–1294 (2014)CrossRefGoogle Scholar
  10. 10.
    Kind, T., Fiehn, O.: Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinf. 8, 20 (2007)CrossRefGoogle Scholar
  11. 11.
    Hakme, E., Lozano, A., Gomez-Ramos, M.M., Hernando, M.D., Fernandez-Alba, A.R.: Non-target evaluation of contaminants in honey bees and pollen samples by gas chromatography time-of-flight mass spectrometry. Chemosphere. 184, 1310–1319 (2017)CrossRefGoogle Scholar
  12. 12.
    Herrera-Lopez, S., Hernando, M.D., Garcia-Calvo, E., Fernandez-Alba, A.R., Ulaszewska, M.M.: Simultaneous screening of targeted and non-targeted contaminants using an LC-QTOF-MS system and automated MS/MS library searching. J. Mass Spectr. 49, 878–893 (2014)CrossRefGoogle Scholar
  13. 13.
    Commission Regulation of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (2002/657/EC). Off. J. Eur. Union. 8–36 (2002)Google Scholar
  14. 14.
    Stolker, A.A.M., Stephany, R.W., van Ginkel, L.A.: Identification of residues by LC-MS. the application of new EU guidelines. Analusis. 28, 947–951 (2000)CrossRefGoogle Scholar
  15. 15.
    Kim, S., Zhang, X.: Discovery of false identification using similarity difference in GC-MS-based metabolomics. J. Chemom. 29, 80–86 (2015)CrossRefGoogle Scholar
  16. 16.
    Bi, H.C., Krausz, K.W., Manna, S.K., Li, F., Johnson, C.H., Gonzalez, F.J.: Optimization of harvesting, extraction, and analytical protocols for UPLC-ESI-MS-based metabolomic analysis of adherent mammalian cancer cells. Anal. Bioanal. Chem. 405, 5279–5289 (2013)CrossRefGoogle Scholar
  17. 17.
    Mezcua, M., Malato, O., Martinez-Uroz, M.A., Lozano, A., Aguera, A., Fernandez-Alba, A.R.: Evaluation of relevant time-of-flight-MS parameters used in HPLC/MS full-scan screening methods for pesticide residues. J. AOAC Int. 94, 1674–1684 (2011)CrossRefGoogle Scholar
  18. 18.
    Mezcua, M., Malato, O., Garcia-Reyes, J.F., Molina-Diaz, A., Fernandez-Alba, A.R.: Accurate-mass databases for comprehensive screening of pesticide residues in food by fast liquid chromatography time-of-flight mass spectrometry. Anal. Chem. 81, 913–929 (2009)CrossRefGoogle Scholar
  19. 19.
    Cajka, T., Fiehn, O.: Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88, 524–545 (2016)CrossRefGoogle Scholar
  20. 20.
    Tian, L., Lin, L., Bayen, S.: Optimization of the post-acquisition data processing for the non-targeted screening of trace leachable residues from reusable plastic bottles by high performance liquid chromatography coupled to hybrid quadrupole time of flight mass spectrometry. Talanta. 193, 70–76 (2019)CrossRefGoogle Scholar
  21. 21.
    Orso, D., Floriano, L., Ribeiro, L.C., Bandeira, N.M.G., Prestes, O.D., Zanella, R.: Simultaneous determination of multiclass pesticides and antibiotics in honey samples based on ultra-high performance liquid chromatography-tandem mass spectrometry. Food Anal. Meth. 9, 1638–1653 (2016)CrossRefGoogle Scholar
  22. 22.
    Al-Alam, J., Fajloun, Z., Chbani, A., Millet, M.: A multiresidue method for the analysis of 90 pesticides, 16 PAHs, and 22 PCBs in honey using QuEChERS-SPME. Anal. Bioanal. Chem. 409, 5157–5169 (2017)CrossRefGoogle Scholar
  23. 23.
    Lo Turco, V., Di Bella, G., Potorti, A.G., Tropea, A., Casale, E.K., Fede, M.R., Dugo, G.: Determination of plasticisers and BPA in Sicilian and Calabrian nectar honeys by selected ion monitoring GC/MS. Food Addit. Contam. Part A-Chem. 33, 1693–1699 (2016)CrossRefGoogle Scholar
  24. 24.
    Malhat, F.M., Haggag, M.N., Loutfy, N.M., Osman, M.A.M., Ahmed, M.T.: Residues of organochlorine and synthetic pyrethroid pesticides in honey, an indicator of ambient environment, a pilot study. Chemosphere. 120, 457–461 (2015)CrossRefGoogle Scholar
  25. 25.
    von Eyken, A., Furlong, D., Arooni, S., Butterworth, F., Roy, J.F., Zweigenbaum, J., Bayen, S.: Direct injection high performance liquid chromatography coupled to data independent acquisition mass spectrometry for the screening of antibiotics in honey. J. Food Drug Anal. (2018).
  26. 26.
    Canadian General Standards Board: Organic production systems. General principles and management standards. National Standard of Canada (CAN/CGSB-32.310-2015), (2015). Retrieved from Accessed 10 Dec 2018
  27. 27.
    Sangster, T., Major, H., Plumb, R., Wilson, A.J., Wilson, I.D.: A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst. 131, 1075–1078 (2006)CrossRefGoogle Scholar
  28. 28.
    Health Canada, List of maximum residue limits (MRLs) for veterinary drugs in foods, in, 2017Google Scholar
  29. 29.
    Fu R.: Selectivity Comparison of Agilent Poroshell 120 Phases in the Separaton of Butter Antioxidants. Agilent Technologies Application Note, (2013). Retrieved from Accessed 10 Dec 2018
  30. 30.
    Zhou, J.T., Yin, Y.X.: Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry. Analyst. 141, 6362–6373 (2016)CrossRefGoogle Scholar
  31. 31.
    Croley, T.R., White, K.D., Callahan, J.H., Musser, S.M.: The chromatographic role in high resolution mass spectrometry for non-targeted analysis. J. Am. Soc. Mass Spectr. 23, 1569–1578 (2012)CrossRefGoogle Scholar
  32. 32.
    Perez-Ortega, P., Lara-Ortega, F.J., Gilbert-Lopez, B., Moreno-Gonzalez, D., Garcia-Reyes, J.F., Molina-Diaz, A.: Screening of over 600 pesticides, veterinary drugs, food-packaging contaminants, mycotoxins, and other chemicals in food by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS). Food Anal. Method. 10, 1216–1244 (2017)CrossRefGoogle Scholar
  33. 33.
    Agilent Technologies: MassHunter Profinder Software B.08.00 User Manual, (2016)Google Scholar
  34. 34.
    Sjerps, R.M.A., Vughs, D., van Leerdam, J.A., ter Laak, T.L., van Wezel, A.P.: Data-driven prioritization of chemicals for various water types using suspect screening LC-HRMS. Water Res. 93, 254–264 (2016)CrossRefGoogle Scholar
  35. 35.
    Rajski, L., Gomez-Ramos, M.D., Fernandez-Alba, A.R.: Simultaneous combination of MS2 workflows for pesticide multiresidue analysis with LC-QOrbitrap. Anal. Methods. 9, 2256–2264 (2017)CrossRefGoogle Scholar
  36. 36.
    Canadian Food Inspection Agency (CFIA): National Chemical Residue Monitoring Program. 2013-2014 Report, (2016). Retrieved from Accessed 10 Dec 2018
  37. 37.
    Schymanski, E.L., Jeon, J., Gulde, R., Fenner, K., Ruff, M., Singer, H.P., Hollender, J.: Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 48, 2097–2098 (2014)CrossRefGoogle Scholar
  38. 38.
    Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. Off. J. Eur. Union, L 125, 10–32 (1996). Retrieved from Accessed 10 Dec 2018
  39. 39.
    Ferrer, I., Thurman, E.M.: Liquid chromatography/time-of-flight/mass spectrometry (LC/TOF/MS) for the analysis of emerging contaminants, Trac-Trend. Anal. Chem. 22, 750–756 (2003)Google Scholar
  40. 40.
    Hernández, F., Ibanez, M., Sancho, J.V., Pozo, O.J.: Comparison of different mass spectrometric techniques combined with liquid chromatography for confirmation of pesticides in environmental water based on the use of identification points. Anal. Chem. 76, 4349–4357 (2004)CrossRefGoogle Scholar
  41. 41.
    Heffernan, A.L., Gomez-Ramos, M.M., Gaus, C., Vijayasarathy, S., Bell, I., Hof, C., Mueller, J.F., Gomez-Ramos, M.J.: Non-targeted, high resolution mass spectrometry strategy for simultaneous monitoring of xenobiotics and endogenous compounds in green sea turtles on the Great Barrier Reef. Sci. Total Environ. 599, 1251–1262 (2017)CrossRefGoogle Scholar
  42. 42.
    Dasenaki, M.E., Bletsou, A.A., Koulis, G.A., Thomaidis, N.S.: Qualitative multiresidue screening method for 143 veterinary drugs and pharmaceuticals in milk and fish tissue using liquid chromatography quadrupole-time-of-flight mass spectrometry. J. Agric. Food Chem. 63, 4493–4508 (2015)CrossRefGoogle Scholar
  43. 43.
    Canadian Food Inspection Agency (CFIA), National Chemical Residue Monitoring Program. 2012-2013 Report (2015). Retrieved from Accessed 10 Dec 2018
  44. 44.
    Tomasini, D., Sampaio, M.R.F., Caldas, S.S., Buffon, J.G., Duarte, F.A., Primel, E.G.: Simultaneous determination of pesticides and 5-hydroxymethylfurfural in honey by the modified QuEChERS method and liquid chromatography coupled to tandem mass spectrometry. Talanta. 99, 380–386 (2012)CrossRefGoogle Scholar
  45. 45.
    Optimizing Sample Preparation for LC/MS/MS of Pesticide Residues in Herbal Teas, Agilent Technologies Application Note, (2013). Retrieved from Accessed 10 Dec 2018
  46. 46.
    Gajda, A., Posyniak, A.: Liquid chromatography - tandem mass spectrometry method for the determination of ten tetracycline residues in muscle samples. Bull. Vet. Inst. Pulawy. 59, 345–352 (2015)CrossRefGoogle Scholar
  47. 47.
    Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., Oda, Y., Kakazu, Y., Kusano, M., Tohge, T., Matsuda, F., Sawada, Y., Hirai, M.Y., Nakanishi, H., Ikeda, K., Akimoto, N., Maoka, T., Takahashi, H., Ara, T., Sakurai, N., Suzuki, H., Shibata, D., Neumann, S., Iida, T., Tanaka, K., Funatsu, K., Matsuura, F., Soga, T., Taguchi, R., Saito, K., Nishioka, T.: MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectr. 45, 703–714 (2010)CrossRefGoogle Scholar
  48. 48.
    Reybroeck, W., Daeseleire, E., De Brabander, H.F., Herman, L.: Antimicrobials in beekeeping. Vet. Microbiol. 158, 1–11 (2012)CrossRefGoogle Scholar
  49. 49.
    Capuano, E., Fogliano, V.: Acrylamide and 5-hydroxymethylfurfural (HMF): a review on metabolism, toxicity, occurrence in food and mitigation strategies. LWT-Food Sci. Technol. 44, 793–810 (2011)CrossRefGoogle Scholar
  50. 50.
    Tornuk, F., Karaman, S., Ozturk, I., Toker, O.S., Tastemur, B., Sagdic, O., Dogan, M., Kayacier, A.: Quality characterization of artisanal and retail Turkish blossom honeys: determination of physicochemical, microbiological, bioactive properties and aroma profile. Ind. Crop. Prod. 46, 124–131 (2013)CrossRefGoogle Scholar
  51. 51.
    da Silva, P.M., Gauche, C., Gonzaga, L.V., Costa, A.C.O., Fett, R.: Honey: chemical composition, stability and authenticity. Food Chem. 196, 309–323 (2016)CrossRefGoogle Scholar
  52. 52.
    Lee, H.S., Nagy, S.: Relative reactivities of sugars in the formation of 5-hydroxymethyl furfural in sugar-catalyst model systems. J. Food Process. Pres. 14, 171–178 (1990)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of Food Science and Agricultural ChemistryMcGill UniversitySainte-Anne-de-BellevueCanada

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